We always adopt the latest technologies, and so does the industry experts. It becomes necessary for each one of us in the technology field to remain updated with the evolving technologies. Considering it as your to-do task, we are up with a course that can earn you a better salary in 2020.
What is Deep Learning?
You all must have heard about robots working as similar to humans. Right? But have you heard about a machine imitating a human brain? No?
Deep Learning is an artificial intelligence method that imitates the working of a human brain to create patterns to help in decision making.
Also known as Deep Neural Learning or deep neural network, is a subset of machine learning that is capable of learning unsupervised data that is unstructured or structured.
A question may arise, that when we had Machine Learning, why there was a need for Deep Learning? To answer the question, we will first have to learn how it works practically.
How Deep Learning Works?
Deep Learning is a concept that evolved with the increase in the usage of digital applications.
In the digital era, we have seen tons of data getting generated every day from various sources throughout the world. Social Media, E-Commerce Applications, or Search Engines are some of the great examples that build data in abundance.
This data becomes available across the world through cloud computing, which is handy for the average population across the globe.
Data that is typically extracted from these cloud applications are unstructured. It will take decades for humans to analyze the data to come to some conclusion.
It led to an increase in the importance of Machine Learning. More and more companies are adopting Artificial Intelligence in analyzing the vast data.
One of the common queries which we have heard from thousands of people is if the Machine Learning was giving the services of analyzing data, then why Deep Learning came into existence.
To understand why it is so important, we will have to differentiate between them. Let us help you to understand the difference with an example.
Deep Learning vs. Machine Learning
Machine Learning is one of the most used digital technologies in the case of fraudulent attacks or data loss. Consider a similar example—an online payment gateway, viz. Xyzpay had a significant loss of $10000.
Using Machine Learning, you will guess where there were some unrecognized steps during the transaction using the patterns generated from the data set.
Deep Learning, being a subset of Machine Learning, utilizes the hierarchical data. Are you confused?
The artificial neural networks are built like a human brain. Like neurons are connected with nerves to form a web, in artificial neural networks, the neural nodes are connected like a web.
While traditional Machine Learning applications were building patterns based on a linear approach, Deep Learning applications create models based on a non-linear path.
So the traditional approach used to count on the amount in the transaction to find the fraudulent behavior. Deep Learning approach checks time, geographical location, IP address, or any other hierarchical data to find fraudulent behavior.
Moving further with the same example of Xyzpay, the first layer in the neural network will process the amount and hand over it to the next layer as output. The second layer will use the production of the first layer and add the user’s IP address in the data and move it to the further tiers.
You may find an improvised after each layer of the neural network. The process continues across all the levels of the neural networks to finally design a pattern.
We hope that the example was quite easy to understand how Deep Learning works.
It brings us to a crucial key takeaway from our discussion.
Why is it essential to learn Deep Learning?
Deep Learning Course is designed by industry leaders to remain aligned with the best practices, which are followed worldwide.
You will be learning Deep Learning with Keras and TensorFlow course, which will help you become familiar with the languages and fundamentals of artificial neural networks, PyTorch, Autoencoders, and many more.
There is an emergence of a phase where developers are more interested in working from home or working independently. Deep Learning will help you to become an expert in creating learning models, draw results, and help you in decision making.
Using all this knowledge, you can build your Deep Learning project. It is recommended to learn the latest technologies. Deep Learning is the newest technology that is useful in developing smartphone apps, power grids, helps in understanding any climatic change, and so on.
The Deep Learning Course ensures your lucrative salaries in 2020.
You may have a chance to work in IT, E-commerce, FinTech, and some other industries, too, where Deep Learning plays an important role.
Companies like Google, American Express, Accenture, IBM, Amazon, or Microsoft are offering a significant hike in the salaries to individuals who are having Deep Learning certificates.
Before you come up with any further queries, let us check out what is the eligibility criteria and pre-requisites for taking Deep Learning Course.
What are the essential eligibility criteria and pre-requisites?
- We have seen a significant boom in demand for engineers with Deep Learning knowledge. It is well suited for the professionals operating at the intermediate and advanced levels.
- Before taking Deep Learning Course, we would recommend you take a Data Scientist Course first. It will become easy for you to understand the concepts of Deep Learning.
- The participants in the Deep Learning Certification Course should have a strong understanding of Machine Learning concepts.
- It requires a minimum system requirement of 8 core processor with a 32 GB RAM. The system requirement is just our recommendation. It is not compulsory. It only ensures a good flow during the sessions.
We would love to conclude our discussion with another definition of Deep Learning.
“Deep Learning mimics the working of a human brain in detecting data patterns and discovering the decisions!”